MSK SPECTRUM data freeze: major cell type embeddings
Florian Uhlitz
2021-04-28
library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(magick, lib.loc = "/home/uhlitzf/miniconda3/lib/R/library")
library(ggpubr)
library(knitr)1 Processing
## load global vars:
source("_src/global_vars.R")
# meta_tbl
# clrs
# markers_v7
# markers_v7_super
# cell_type_super_lookup
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "\\.", " ")
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "Ovarian", "Ov")
## load data --------------------------------------
## load cohort embeddings
seu_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/outs_pre/cells.tsv") %>%
mutate(cell_type = ifelse(cell_type == "Monocyte", "Myeloid.cell", cell_type)) %>%
mutate(cell_type = ifelse(cell_type == "Ovarian.cancer.cell", "Ov.cancer.cell", cell_type))
# seu_tbl <- read_tsv("/work/shah/isabl_data_lake/analyses/68/74/6874/cells.tsv") %>%
# mutate(cell_type = ifelse(cell_type == "Monocyte", "Myeloid.cell", cell_type)) %>%
# mutate(cell_type = ifelse(cell_type == "Ovarian.cancer.cell", "Ov.cancer.cell", cell_type))
## load consensus data
consOV_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/consensusOV/SPECTRUM_freeze_v7_consensusOV.tsv") %>%
mutate(consensusOV = ordered(consensusOV, levels = names(clrs$consensusOV))) %>%
select(cell_id, consensusOV)
## join data
seu_tbl_full <- seu_tbl %>%
left_join(meta_tbl, by = "sample") %>%
filter(therapy == "pre-Rx", cell_type != "Other", tumor_supersite != "Unknown") %>%
rename(UMAP_1 = umap50_1, UMAP_2 = umap50_2) %>%
## flip UMAP coordinates?
# mutate(UMAP_1 = -UMAP_1, UMAP_2 = -UMAP_2) %>%
mutate(cell_type_super = cell_type_super_lookup[cell_type]) %>%
mutate(cell_type = ordered(str_replace_all(cell_type, "\\.", " "),
levels = names(clrs$cell_type))) %>%
mutate(sort_short_x = ifelse(sort_short == "U" & cell_type_super == "Immune",
"CD45+", ifelse(sort_short == "U" & cell_type_super == "Stromal",
"CD45-", sort_short))) %>%
left_join(consOV_tbl, by = "cell_id")# ## extract cell cycle tbl
# cell_cycle_list <- as.list(rep(NA, times = length(names(clrs$cluster_label)))) %>%
# setNames(names(clrs$cluster_label))
#
# for (i in 1:length(cell_cycle_list)) {
# seu_obj <- read_rds(
# paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/outs_pre/",
# names(cell_cycle_list)[i], "_seurat_0.1.rds"))
# cell_cycle_list[[i]] <- as_tibble(FetchData(seu_obj, c("cell_id", "S.Score", "G2M.Score", "CC.Diff", "Phase")))
# rm(seu_obj)
# }
#
# cell_cycle_tbl <- bind_rows(cell_cycle_list)
#
# write_tsv(cell_cycle_tbl, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/cell_cycle_tbl.tsv")
cell_cycle_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/cell_cycle_tbl.tsv")
seu_tbl_full <- seu_tbl_full %>%
left_join(cell_cycle_tbl, by = "cell_id")
# seu_tbl_full <- seu_tbl_full %>%
# sample_n(10000)1.1 UMAPs
dpi <- 72
rw <- 10
rh <- 10
base_umap <- ggplot(seu_tbl_full) +
coord_fixed() +
NoAxes() +
theme(legend.position = c(0, 1),
legend.justification = c("left", "top"),
legend.box.just = "left",
legend.margin = ggplot2::margin(1, 1, 1, 1),
#panel.border = element_rect(linetype = 1, color = "black", size = 1),
legend.text = element_text(size = 14, margin = ggplot2::margin(0, 10, 0, 0)),
legend.spacing.x = unit(0, "npc"),
legend.title = element_blank(),
plot.title = element_text(hjust = 0.5, vjust = 0.5, face = "plain", size = 22))
pt.size <- 0.1
pt.size.mini <- 0.01
pt.alpha <- 0.05
pt.alpha.mini <- 0.02
# median_tbl <- seu_tbl_full %>%
# filter(cell_type != "Other") %>%
# group_by(cell_type, reduction) %>%
# summarise(UMAP_1 = median(UMAP_1),
# UMAP_2 = median(UMAP_2))
ncol1_legend <- list(
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")),
theme(legend.text = element_text(size = 14, margin = ggplot2::margin(0, 10, 9.5, 0), color = "white"),
legend.spacing.x = unit(0, "npc"))
)
umap_site <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = tumor_supersite), size = pt.size.mini, alpha = pt.alpha.mini, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
scale_color_manual(values = clrs$tumor_supersite) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 2,
label.position = "right"))
umap_site_mini <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = tumor_supersite), size = pt.size.mini, alpha = pt.alpha.mini, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
scale_color_manual(values = clrs$tumor_supersite) +
guides(color = F)
umap_site_legend_ncol1 <- cowplot::get_legend(umap_site + ncol1_legend)
umap_site_legend <- cowplot::get_legend(umap_site)
umap_cell_type <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = fct_rev(cell_type)), size = pt.size, alpha = pt.alpha, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
scale_color_manual(values = clrs$cell_type) +
ncol1_legend
umap_cell_type_mini <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = cell_type), size = pt.size.mini, alpha = pt.alpha.mini, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
scale_color_manual(values = clrs$cell_type) +
guides(color = F)
umap_cell_type_legend <- cowplot::get_legend(umap_cell_type)
umap_patient <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = patient_id_short), size = pt.size, alpha = pt.alpha, raster.dpi = 200, raster.width = 10, raster.height = 10) +
scale_color_manual(values = clrs$patient_id_short) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
nrow = 17,
label.position = "right")) +
theme(legend.text = element_text(size = 14, margin = ggplot2::margin(0, 10, 0, 0)),
legend.spacing.x = unit(0, "npc"))
umap_patient_mini <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = patient_id_short), size = pt.size.mini, alpha = pt.alpha.mini, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
scale_color_manual(values = clrs$patient_id_short) +
guides(color = F)
# umap_patient_legend <- cowplot::get_legend(umap_patient)
umap_consOV <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = consensusOV), size = pt.size, alpha = pt.alpha, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
scale_color_manual(values = clrs$consensusOV, labels = c("Immuno.", "Mesench.", "Prolif.", "Differen.")) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 2,
label.position = "right")) +
theme(legend.text = element_text(size = 14, margin = ggplot2::margin(0, 10, 0, 0)),
legend.spacing.x = unit(0, "npc"))
umap_consOV_mini <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = consensusOV), size = pt.size.mini, alpha = pt.alpha.mini, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
scale_color_manual(values = clrs$consensusOV) +
guides(color = F)
umap_consOV_legend <- cowplot::get_legend(umap_consOV)
umap_mutsig <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = consensus_signature), size = pt.size, alpha = pt.alpha, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
scale_color_manual(values = clrs$consensus_signature) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 2,
label.position = "right")) +
theme(legend.text = element_text(size = 14, margin = ggplot2::margin(0, 10, 0, 0)),
legend.spacing.x = unit(0, "npc"))
umap_mutsig_mini <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = consensus_signature), size = pt.size.mini, alpha = pt.alpha.mini, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
scale_color_manual(values = clrs$consensus_signature) +
guides(color = F)
umap_mutsig_legend <- cowplot::get_legend(umap_mutsig)
umap_phase <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = Phase), size = pt.size, alpha = pt.alpha, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
scale_color_manual(values = clrs$Phase) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")) +
theme(legend.text = element_text(size = 14, margin = ggplot2::margin(0, 10, 0, 0)),
legend.spacing.x = unit(0, "npc")) +
labs(title = "Phase", color = "")
continuous_umap_layers <- list(
scale_color_viridis_c(),
guides(color = guide_colorbar(label.position = "right",
title.position = "top",
title.hjust = 0,
title.vjust = 1,
direction = "vertical")),
theme(legend.title = element_text(face = "bold", size = 14),
legend.key.height = unit(0.05, "npc"),
legend.key.width = unit(0.03, "npc"),
legend.position = c(-0.07, 1),
legend.justification = c("left", "top"),
plot.title = element_text(hjust = 0.5, vjust = 0.5, face = "plain", size = 18)))
umap_umis <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = log2(nCount_RNA)), size = pt.size, alpha = pt.alpha, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
labs(title = "log2(#UMIs)", color = "") +
continuous_umap_layers
umap_genes <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = log2(nFeature_RNA)), size = pt.size, alpha = pt.alpha, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
labs(title = "log2(#Genes)", color = "") +
continuous_umap_layers
umap_mitos <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = percent.mt), size = pt.size, alpha = pt.alpha, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
labs(title = "MT reads [%]", color = "") +
continuous_umap_layers
umap_doublet_score <- base_umap +
ggrastr::geom_point_rast(aes(UMAP_1, UMAP_2, color = doublet_score), size = pt.size, alpha = pt.alpha, raster.dpi = dpi, raster.width = rw, raster.height = rh) +
labs(title = "Doublet score", color = "") +
continuous_umap_layers1.2 total numbers
total_numbers_plot <- function(column_var) {
column_var <- enquo(column_var)
plot_data <- seu_tbl_full %>%
filter(cell_type != "Other", therapy == "pre-Rx") %>%
group_by(!!column_var) %>%
tally %>%
arrange(n) %>%
mutate(column_label = paste0(!!column_var, " (", format(n, trim=T, big.mark=","), ")")) %>%
mutate(!!column_var := ordered(!!column_var, levels = unique(!!column_var)))
common_layers <- list(
scale_y_continuous(expand = c(0, 0)),
guides(fill = F, color = F),
coord_flip(clip = "off"),
theme(axis.title = element_blank(),
axis.text = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
plot.title = element_text(face = "plain"))
)
p1 <- ggplot(plot_data) +
geom_point(aes(!!column_var, 0, color = !!column_var), size = 4) +
scale_color_manual(values = clrs[[as_label(column_var)]]) +
common_layers
p2 <- ggplot(plot_data) +
geom_bar(aes(!!column_var, n, fill = !!column_var), stat = "identity", width = 0.3) +
geom_text(aes(!!column_var, 1, label = column_label), hjust = 0, vjust = 0, nudge_x = 0.2) +
scale_fill_manual(values = clrs[[as_label(column_var)]]) +
common_layers
plot_grid(p1, p2, align = "v", ncol = 2, rel_widths = c(0.03, 0.97))
}
numbers_plot_cell_type <- total_numbers_plot(cell_type)
numbers_plot_tumor_supersite <- total_numbers_plot(tumor_supersite)1.3 Mixing per cell type
pm_score_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v7/patient_mixing_scores.tsv") %>%
mutate(cell_type = str_replace_all(cell_type, "Monocyte", "Myeloid cell")) %>%
mutate(cell_type = str_replace_all(cell_type, "Ovarian.cancer.cell", "Ov cancer cell")) %>%
mutate(patient_id_short = str_sub(cell_id, 13, 15)) %>%
left_join(distinct(meta_tbl, patient_id_short, .keep_all = T), by = "patient_id_short") %>%
filter(cell_type != "Other")
pm_score_tbl_ordered <- pm_score_tbl %>%
filter(cell_type != "Other") %>%
group_by(cell_type) %>%
mutate(median = median(score)) %>%
ungroup() %>%
arrange(-median) %>%
mutate(cell_type = ordered(cell_type, levels = unique(cell_type)))
mixing_plot_cell_type <- ggplot(pm_score_tbl_ordered) +
geom_violin(aes(cell_type, score, fill = cell_type),
adjust = 3, color = "white", width = 1) +
geom_boxplot(aes(cell_type, score, color = cell_type),
width = 0.2, size = 1, fill = NA, outlier.shape = NA) +
geom_boxplot(aes(cell_type, score, fill = cell_type),
width = 0.2, color = "white", outlier.shape = NA) +
geom_hline(yintercept = 0, linetype = 2, size = 0.5, color = "black") +
scale_fill_manual(values = clrs$cell_type) +
scale_color_manual(values = clrs$cell_type) +
guides(fill = F, color = F) +
scale_y_continuous(expand = c(0, 0),
limits = c(-5, 7),
breaks = c(-4, -2, 0, 2, 4, 6)) +
labs(y = "Patient\nspecificity") +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1),
axis.title.x = element_blank())
# ggsave_nodb("_fig/_paper/01_patient_mixing_plot_v5.pdf", mixing_plot_cell_type,
# width = 3, height = 5)
ggplot(pm_score_tbl) +
geom_violin(aes(consensus_signature, score, fill = consensus_signature),
adjust = 3, color = "white", width = 1) +
geom_boxplot(aes(consensus_signature, score, color = consensus_signature),
width = 0.2, size = 1, fill = NA, outlier.size = 0.01) +
geom_boxplot(aes(consensus_signature, score, fill = consensus_signature),
width = 0.2, color = "white", outlier.shape = NA) +
geom_hline(yintercept = 0, linetype = 2, size = 0.5, color = "black") +
scale_fill_manual(values = clrs$consensus_signature) +
scale_color_manual(values = clrs$consensus_signature) +
facet_wrap(~cell_type, scales = "free") +
guides(fill = F, color = F) +
scale_y_continuous(expand = c(0, 0),
limits = c(-5, 7),
breaks = c(-4, -2, 0, 2, 4, 6)) +
labs(y = "Patient\nspecificity") +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1),
axis.title.x = element_blank())
ggsave_nodb("_fig/002_cohort/002_patient_mixing_mutsig.pdf", width = 6, height = 9)
ggsave("_fig/002_cohort/002_patient_mixing_mutsig.png", width = 6, height = 9)_fig/002_cohort/002_patient_mixing_mutsig.pdf
_fig/002_cohort/002_patient_mixing_mutsig.png
ggplot(pm_score_tbl) +
geom_violin(aes(patient_id_short, score, fill = consensus_signature),
adjust = 3, color = "white", width = 1) +
geom_boxplot(aes(patient_id_short, score, color = consensus_signature),
width = 0.2, size = 1, fill = NA, outlier.size = 0.01) +
geom_boxplot(aes(patient_id_short, score, fill = consensus_signature),
width = 0.2, color = "white", outlier.shape = NA) +
geom_hline(yintercept = 0, linetype = 2, size = 0.5, color = "black") +
scale_fill_manual(values = clrs$consensus_signature) +
scale_color_manual(values = clrs$consensus_signature) +
facet_wrap(~cell_type, scales = "free") +
guides(fill = F, color = F) +
scale_y_continuous(expand = c(0, 0),
limits = c(-5, 7),
breaks = c(-4, -2, 0, 2, 4, 6)) +
labs(y = "Patient\nspecificity") +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1),
axis.title.x = element_blank())
ggsave_nodb("_fig/002_cohort/002_patient_mixing_mutsig_patient.pdf", width = 18, height = 9)
ggsave("_fig/002_cohort/002_patient_mixing_mutsig_patient.png", width = 18, height = 9)
1.4 pairwise comparisons
### pairwise comparisons
pm_score_summary <- pm_score_tbl_ordered %>%
separate(cell_id, into = c("patient_id", "sample_suffix"), remove = F, extra = "drop", sep = "_") %>%
group_by(patient_id, cell_type) %>%
summarise(mean_score = mean(score))
ptt_mat <- pairwise.t.test(pm_score_summary$mean_score, pm_score_summary$cell_type, p.adjust.method = "fdr") %$%
p.value
ptt_mat_full <- matrix(nrow = 9, ncol = 9)
rownames(ptt_mat_full) <- unique(c(colnames(ptt_mat), rownames(ptt_mat)))
colnames(ptt_mat_full) <- unique(c(colnames(ptt_mat), rownames(ptt_mat)))
ptt_mat_full[upper.tri(ptt_mat_full)] <- 1
ptt_mat_full[lower.tri(ptt_mat_full)] <- ptt_mat[lower.tri(ptt_mat, diag = T)]
diag(ptt_mat_full) <- 1
ptt_tbl <- ptt_mat_full %>%
as_tibble(rownames = "cell_type_1") %>%
gather(cell_type_2, pvalue, -cell_type_1) %>%
mutate(cell_type_2 = ordered(cell_type_2, levels = levels(pm_score_tbl_ordered$cell_type))) %>%
mutate(cell_type_1 = ordered(cell_type_1, levels = levels(pm_score_tbl_ordered$cell_type))) %>%
mutate(log10p = -log10(pvalue),
sig = pvalue < 0.1,
label = ifelse(sig, "*", "")) %>%
replace_na(list(pvalue = 1, log10p = 0, sig = F, label = ""))
pm_comparisons <- ggplot(ptt_tbl) +
# geom_tile(aes(fct_rev(cell_type_1), fct_rev(cell_type_2), fill = log10p)) +
geom_point(aes(fct_rev(cell_type_1), fct_rev(cell_type_2), size = log10p), color = "black", shape = 21) +
geom_point(aes(fct_rev(cell_type_1), fct_rev(cell_type_2), fill = log10p, size = log10p), shape = 21) +
# geom_point(aes(fct_rev(cell_type_1), fct_rev(cell_type_2), shape = sig),
# color = "white", size = 5) +
# geom_point(aes(fct_rev(cell_type_1), fct_rev(cell_type_2), shape = sig),
# color = "black", size = 3) +
scale_fill_gradientn(colours = viridis(9)) +
scale_size_continuous(guide = F, range = c(-1, 5)) +
# scale_color_gradientn(colours = viridis(9)) +
theme(aspect.ratio = 1,
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.title = element_blank(),
plot.title = element_text(face = "plain", size = 12)) +
scale_shape_manual(values = c("", "*"), guide = F) +
# scale_x_discrete(expand = c(0, 0)) +
# scale_y_discrete(expand = c(0, 0)) +
labs(fill = "-log10(q-value)", title = "Pairwise comparisons\nof patient specificity")
pm_comparisons
print(ptt_tbl, n = 100)## # A tibble: 81 x 6
## cell_type_1 cell_type_2 pvalue log10p sig label
## <ord> <ord> <dbl> <dbl> <lgl> <chr>
## 1 Ov cancer cell Ov cancer cell 1.00e+ 0 0 FALSE ""
## 2 Fibroblast Ov cancer cell 9.02e-46 45.0 TRUE "*"
## 3 Myeloid cell Ov cancer cell 2.03e-62 61.7 TRUE "*"
## 4 T cell Ov cancer cell 3.89e-79 78.4 TRUE "*"
## 5 Dendritic cell Ov cancer cell 1.12e-86 86.0 TRUE "*"
## 6 Plasma cell Ov cancer cell 5.60e-85 84.3 TRUE "*"
## 7 Endothelial cell Ov cancer cell 2.90e-84 83.5 TRUE "*"
## 8 B cell Ov cancer cell 2.35e-89 88.6 TRUE "*"
## 9 Mast cell Ov cancer cell 9.17e-98 97.0 TRUE "*"
## 10 Ov cancer cell Fibroblast 1.00e+ 0 0 FALSE ""
## 11 Fibroblast Fibroblast 1.00e+ 0 0 FALSE ""
## 12 Myeloid cell Fibroblast 1.02e- 4 3.99 TRUE "*"
## 13 T cell Fibroblast 1.11e-14 14.0 TRUE "*"
## 14 Dendritic cell Fibroblast 4.81e-21 20.3 TRUE "*"
## 15 Plasma cell Fibroblast 1.75e-19 18.8 TRUE "*"
## 16 Endothelial cell Fibroblast 8.01e-19 18.1 TRUE "*"
## 17 B cell Fibroblast 1.33e-23 22.9 TRUE "*"
## 18 Mast cell Fibroblast 3.67e-32 31.4 TRUE "*"
## 19 Ov cancer cell Myeloid cell 1.00e+ 0 0 FALSE ""
## 20 Fibroblast Myeloid cell 1.00e+ 0 0 FALSE ""
## 21 Myeloid cell Myeloid cell 1.00e+ 0 0 FALSE ""
## 22 T cell Myeloid cell 7.51e- 5 4.12 TRUE "*"
## 23 Dendritic cell Myeloid cell 4.93e- 9 8.31 TRUE "*"
## 24 Plasma cell Myeloid cell 6.16e- 8 7.21 TRUE "*"
## 25 Endothelial cell Myeloid cell 1.74e- 7 6.76 TRUE "*"
## 26 B cell Myeloid cell 6.57e-11 10.2 TRUE "*"
## 27 Mast cell Myeloid cell 1.03e-17 17.0 TRUE "*"
## 28 Ov cancer cell T cell 1.00e+ 0 0 FALSE ""
## 29 Fibroblast T cell 1.00e+ 0 0 FALSE ""
## 30 Myeloid cell T cell 1.00e+ 0 0 FALSE ""
## 31 T cell T cell 1.00e+ 0 0 FALSE ""
## 32 Dendritic cell T cell 6.07e- 2 1.22 TRUE "*"
## 33 Plasma cell T cell 1.59e- 1 0.797 FALSE ""
## 34 Endothelial cell T cell 2.19e- 1 0.659 FALSE ""
## 35 B cell T cell 9.26e- 3 2.03 TRUE "*"
## 36 Mast cell T cell 1.45e- 6 5.84 TRUE "*"
## 37 Ov cancer cell Dendritic cell 1.00e+ 0 0 FALSE ""
## 38 Fibroblast Dendritic cell 1.00e+ 0 0 FALSE ""
## 39 Myeloid cell Dendritic cell 1.00e+ 0 0 FALSE ""
## 40 T cell Dendritic cell 1.00e+ 0 0 FALSE ""
## 41 Dendritic cell Dendritic cell 1.00e+ 0 0 FALSE ""
## 42 Plasma cell Dendritic cell 6.59e- 1 0.181 FALSE ""
## 43 Endothelial cell Dendritic cell 5.30e- 1 0.276 FALSE ""
## 44 B cell Dendritic cell 5.04e- 1 0.298 FALSE ""
## 45 Mast cell Dendritic cell 3.76e- 3 2.42 TRUE "*"
## 46 Ov cancer cell Plasma cell 1.00e+ 0 0 FALSE ""
## 47 Fibroblast Plasma cell 1.00e+ 0 0 FALSE ""
## 48 Myeloid cell Plasma cell 1.00e+ 0 0 FALSE ""
## 49 T cell Plasma cell 1.00e+ 0 0 FALSE ""
## 50 Dendritic cell Plasma cell 1.00e+ 0 0 FALSE ""
## 51 Plasma cell Plasma cell 1.00e+ 0 0 FALSE ""
## 52 Endothelial cell Plasma cell 8.36e- 1 0.0777 FALSE ""
## 53 B cell Plasma cell 2.58e- 1 0.589 FALSE ""
## 54 Mast cell Plasma cell 7.89e- 4 3.10 TRUE "*"
## 55 Ov cancer cell Endothelial cell 1.00e+ 0 0 FALSE ""
## 56 Fibroblast Endothelial cell 1.00e+ 0 0 FALSE ""
## 57 Myeloid cell Endothelial cell 1.00e+ 0 0 FALSE ""
## 58 T cell Endothelial cell 1.00e+ 0 0 FALSE ""
## 59 Dendritic cell Endothelial cell 1.00e+ 0 0 FALSE ""
## 60 Plasma cell Endothelial cell 1.00e+ 0 0 FALSE ""
## 61 Endothelial cell Endothelial cell 1.00e+ 0 0 FALSE ""
## 62 B cell Endothelial cell 1.91e- 1 0.719 FALSE ""
## 63 Mast cell Endothelial cell 3.82e- 4 3.42 TRUE "*"
## 64 Ov cancer cell B cell 1.00e+ 0 0 FALSE ""
## 65 Fibroblast B cell 1.00e+ 0 0 FALSE ""
## 66 Myeloid cell B cell 1.00e+ 0 0 FALSE ""
## 67 T cell B cell 1.00e+ 0 0 FALSE ""
## 68 Dendritic cell B cell 1.00e+ 0 0 FALSE ""
## 69 Plasma cell B cell 1.00e+ 0 0 FALSE ""
## 70 Endothelial cell B cell 1.00e+ 0 0 FALSE ""
## 71 B cell B cell 1.00e+ 0 0 FALSE ""
## 72 Mast cell B cell 2.98e- 2 1.53 TRUE "*"
## 73 Ov cancer cell Mast cell 1.00e+ 0 0 FALSE ""
## 74 Fibroblast Mast cell 1.00e+ 0 0 FALSE ""
## 75 Myeloid cell Mast cell 1.00e+ 0 0 FALSE ""
## 76 T cell Mast cell 1.00e+ 0 0 FALSE ""
## 77 Dendritic cell Mast cell 1.00e+ 0 0 FALSE ""
## 78 Plasma cell Mast cell 1.00e+ 0 0 FALSE ""
## 79 Endothelial cell Mast cell 1.00e+ 0 0 FALSE ""
## 80 B cell Mast cell 1.00e+ 0 0 FALSE ""
## 81 Mast cell Mast cell 1.00e+ 0 0 FALSE ""ggsave_nodb("_fig/002_cohort/002_patient_mixing_tests.pdf", pm_comparisons, width = 5, height = 4)
ggsave("_fig/002_cohort/002_patient_mixing_tests.png", pm_comparisons, width = 5, height = 4)
1.5 consensusOV summary
consOV_tbl_summary <- seu_tbl_full %>%
select(cell_type, consensusOV, patient_id_short, tumor_supersite) %>%
mutate(consOV_cell_type = cell_type) %>%
# mutate(consOV_cell_type = ifelse(S.Score > 0.5, "Cycling cell", as.character(cell_type))) %>%
group_by(consOV_cell_type, consensusOV) %>%
tally %>%
group_by(consOV_cell_type) %>%
mutate(nrel = n/(sum(n))*100) %>%
ungroup %>%
mutate(consensusOV = ordered(consensusOV, levels = c(names(clrs$consensusOV)))) %>%
arrange(consensusOV, nrel)
heat_layers <- list(
geom_tile(),
scale_fill_gradientn(colors = magma(9)[c(-1)], limits = c(0, 100), breaks = c(0, 50, 100)),
theme(
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1),
legend.position = "bottom",
aspect.ratio = 4/9
),
guides(fill = guide_colorbar(title.position = "top"))
)
tcga_heat <- consOV_tbl_summary %>%
filter(consOV_cell_type != "Other") %>%
mutate(cell_type = ordered(consOV_cell_type, levels = unique(c("Myeloid cell", "Fibroblast", "Cycling cell", names(clrs$cell_type))))) %>%
mutate(consensusOV = ordered(consensusOV, levels = names(clrs$consensusOV))) %>%
ggplot(aes(cell_type, consensusOV, fill = nrel)) +
labs(fill = "Fraction\nof cells [%]") +
heat_layers
tcga_heat_wide <- consOV_tbl_summary %>%
filter(consOV_cell_type != "Other") %>%
mutate(cell_type = ordered(consOV_cell_type, levels = unique(c("Myeloid cell", "Fibroblast", "Cycling cell", names(clrs$cell_type))))) %>%
mutate(consensusOV = ordered(consensusOV, levels = names(clrs$consensusOV))) %>%
ggplot(aes(consensusOV, cell_type, fill = nrel)) +
labs(fill = "Fraction\nof cells [%]") +
heat_layers +
theme(aspect.ratio = 9/4, legend.position = "right")
# tcga_heat + guides(fill = F)
tcga_heat_legend <- get_legend(tcga_heat)
print(consOV_tbl_summary, n = 36)## # A tibble: 36 x 4
## consOV_cell_type consensusOV n nrel
## <ord> <ord> <int> <dbl>
## 1 Fibroblast Immunoreactive 809 0.499
## 2 Ov cancer cell Immunoreactive 3125 1.24
## 3 Endothelial cell Immunoreactive 260 1.40
## 4 Plasma cell Immunoreactive 1129 5.39
## 5 Mast cell Immunoreactive 99 7.88
## 6 B cell Immunoreactive 1857 9.94
## 7 T cell Immunoreactive 43985 17.6
## 8 Dendritic cell Immunoreactive 1038 21.6
## 9 Myeloid cell Immunoreactive 154098 76.6
## 10 Ov cancer cell Mesenchymal 1366 0.542
## 11 Mast cell Mesenchymal 13 1.03
## 12 Dendritic cell Mesenchymal 63 1.31
## 13 B cell Mesenchymal 724 3.88
## 14 Endothelial cell Mesenchymal 950 5.13
## 15 T cell Mesenchymal 13311 5.32
## 16 Myeloid cell Mesenchymal 12984 6.45
## 17 Plasma cell Mesenchymal 1550 7.40
## 18 Fibroblast Mesenchymal 101439 62.6
## 19 Myeloid cell Proliferative 1329 0.660
## 20 Mast cell Proliferative 50 3.98
## 21 Dendritic cell Proliferative 375 7.79
## 22 T cell Proliferative 21149 8.45
## 23 Fibroblast Proliferative 19280 11.9
## 24 Ov cancer cell Proliferative 49008 19.5
## 25 Plasma cell Proliferative 4117 19.7
## 26 B cell Proliferative 4339 23.2
## 27 Endothelial cell Proliferative 4388 23.7
## 28 Myeloid cell Differentiated 32806 16.3
## 29 Fibroblast Differentiated 40550 25.0
## 30 B cell Differentiated 11753 62.9
## 31 Plasma cell Differentiated 14148 67.6
## 32 T cell Differentiated 171890 68.7
## 33 Dendritic cell Differentiated 3338 69.3
## 34 Endothelial cell Differentiated 12933 69.8
## 35 Ov cancer cell Differentiated 198338 78.8
## 36 Mast cell Differentiated 1095 87.12 Figure 2 upper panel
2.1 v7.1
arrow <- arrow(angle = 20, type = "closed", length = unit(0.1, "npc"))
umap_coord_anno <- ggplot(tibble(group = c("UMAP1", "UMAP2"),
x = c(0, 0), xend = c(1, 0),
y = c(0, 0), yend = c(0, 1),
lx = c(0.5, -0.15), ly = c(-0.15, 0.5),
angle = c(0, 90))) +
geom_segment(aes(x, y, xend = xend, yend = yend, group = group),
arrow = arrow, size = 1, lineend = "round") +
geom_text(aes(lx, ly, label = group, angle = angle), size = 4) +
theme_void() +
coord_fixed(xlim = c(-0.3, 1), ylim = c(-0.3, 1))
add_umap_coord <- function(gg_obj) {
p <- ggdraw() +
draw_plot(gg_obj, x = 0, y = 0, width = 1, height = 1) +
draw_plot(umap_coord_anno, x = -0.015, y = -0.02, width = 0.2, height = 0.2)
return(p)
}
full_grid1 <- ggdraw() +
## left
draw_label("Patient", x = 0.25, y = 0.98, size = 20) +
draw_plot(add_umap_coord(umap_patient), x = 0, y = 0, width = 0.5, height = 1) +
draw_label(paste0("n = ", nrow(seu_tbl_full)),
x = 0.25, y = 0.03, size = 16, hjust = 0) +
## right upper
draw_plot(mixing_plot_cell_type, x = 0.5+0.5*0, y = 0.66, width = 0.5*0.3, height = 0.33) +
draw_label("Cell type", x = 0.71, y = 0.98, size = 20, hjust = 0) +
draw_plot(umap_cell_type_mini, x = 0.83, y = 0.68, width = 0.5*1/3, height = 0.31) +
draw_plot(numbers_plot_cell_type, x = 0.7, y = 0.651, width = 0.13, height = 0.315) +
## right middle
draw_label("TCGA", x = 0.53+0.5*0, y = 0.6, size = 20, hjust = 0) +
draw_plot(umap_consOV_mini, x = 0.5+0.5*0, y = 0.3, width = 0.5*1/3, height = 0.31) +
draw_label("Signature", x = 0.53+0.5*0.33, y = 0.6, size = 20, hjust = 0) +
draw_plot(umap_mutsig_mini, x = 0.5+0.5*0.33, y = 0.3, width = 0.5*1/3, height = 0.31) +
draw_label("Site", x = 0.53+0.5*0.66, y = 0.6, size = 20, hjust = 0) +
draw_plot(umap_site_mini, x = 0.5+0.5*0.66, y = 0.3, width = 0.5*1/3, height = 0.31) +
## right lower
draw_grob(umap_consOV_legend, x = 0.52+0.5*0, y = -0.67, hjust = 0, vjust = 0) +
draw_plot(tcga_heat + guides(fill = F), x = 0.49+0.5*0, y = -0.03, width = 0.18, height = 0.3) +
draw_grob(tcga_heat_legend, x = 0.68, y = -0.35) +
draw_grob(umap_mutsig_legend, x = 0.52+0.5*0.33, y = -0.67, hjust = 0, vjust = 0) +
draw_grob(umap_site_legend, x = 0.52+0.5*0.66, y = -0.67, hjust = 0, vjust = 0)
full_grid1
full_grid1_rds <- list(rds = full_grid1, width = 20, height = 10)
write_rds(full_grid1, "_fig/002_cohort/002_cohort_umaps_v7.1.rds")
ggsave(filename = "_fig/002_cohort/002_cohort_umaps_v7.1.png", full_grid1, width = 20, height = 10)
ggsave_nodb(filename = "_fig/002_cohort/002_cohort_umaps_v7.1.pdf", full_grid1, width = 20, height = 10)
2.2 v7.2
full_grid2 <- ggdraw() +
## left
draw_label("Patient", x = 0.25, y = 0.98, size = 20) +
draw_plot(add_umap_coord(umap_patient), x = 0, y = 0, width = 0.5, height = 1) +
draw_label(paste0("n = ", nrow(seu_tbl_full)),
x = 0.25, y = 0.03, size = 16, hjust = 0) +
## right upper
draw_plot(mixing_plot_cell_type, x = 0.5+0.5*0, y = 0.66, width = 0.5*0.3, height = 0.33) +
draw_label("Cell type", x = 0.725, y = 0.98, size = 20, hjust = 0) +
draw_plot(umap_cell_type_mini, x = 0.83, y = 0.68, width = 0.5*1/3, height = 0.31) +
draw_plot(numbers_plot_cell_type, x = 0.715, y = 0.651, width = 0.13, height = 0.315) +
## right middle
draw_label("TCGA", x = 0.53+0.5*0, y = 0.6, size = 20, hjust = 0) +
draw_plot(umap_consOV_mini, x = 0.5+0.5*0, y = 0.3, width = 0.5*1/3, height = 0.31) +
draw_label("Site", x = 0.725, y = 0.6, size = 20, hjust = 0) +
draw_plot(numbers_plot_tumor_supersite, x = 0.715, y = 0.34, width = 0.13, height = 0.25) +
draw_plot(umap_site_mini, x = 0.5+0.5*0.66, y = 0.3, width = 0.5*1/3, height = 0.31) +
## right lower
draw_plot(tcga_heat + guides(fill = F), x = 0.49+0.5*0, y = -0.03, width = 0.18, height = 0.3) +
draw_grob(tcga_heat_legend, x = 0.68, y = -0.35) +
draw_grob(umap_consOV_legend, x = 0.52+0.5*0, y = -0.67, hjust = 0, vjust = 0)
full_grid2
full_grid2_rds <- list(rds = full_grid2, width = 20, height = 10)
write_rds(full_grid2, "_fig/002_cohort/002_cohort_umaps_v7.2.rds")
ggsave(filename = "_fig/002_cohort/002_cohort_umaps_v7.2.png", full_grid2, width = 20, height = 10)
ggsave_nodb(filename = "_fig/002_cohort/002_cohort_umaps_v7.2.pdf", full_grid2, width = 20, height = 10)
2.3 v7.3
full_grid3 <- ggdraw() +
## left
draw_label("Patient", x = 0.25, y = 0.98, size = 20) +
draw_plot(add_umap_coord(umap_patient), x = 0, y = 0, width = 0.5, height = 1) +
draw_label(paste0("n = ", nrow(seu_tbl_full)),
x = 0.25, y = 0.03, size = 16, hjust = 0) +
## right upper
draw_label("Cell type", x = 0.53+0.5*0, y = 0.98, size = 20, hjust = 0) +
draw_plot(numbers_plot_cell_type, x = 0.52+0.5*0 , y = 0.651, width = 0.13, height = 0.315) +
draw_plot(umap_cell_type_mini, x = 0.64, y = 0.68, width = 0.5*1/3, height = 0.31) +
draw_plot(mixing_plot_cell_type, x = 0.83, y = 0.66, width = 0.5*0.3, height = 0.33) +
## right middle
draw_label("TCGA", x = 0.53+0.5*0, y = 0.6, size = 20, hjust = 0) +
draw_plot(umap_consOV_mini, x = 0.5+0.5*0, y = 0.3, width = 0.5*1/3, height = 0.31) +
draw_label("Site", x = 0.725, y = 0.6, size = 20, hjust = 0) +
draw_plot(numbers_plot_tumor_supersite, x = 0.715, y = 0.34, width = 0.13, height = 0.25) +
draw_plot(umap_site_mini, x = 0.5+0.5*0.66, y = 0.3, width = 0.5*1/3, height = 0.31) +
## right lower
draw_plot(tcga_heat + guides(fill = F), x = 0.49+0.5*0, y = -0.03, width = 0.18, height = 0.3) +
draw_grob(tcga_heat_legend, x = 0.68, y = -0.35) +
draw_grob(umap_consOV_legend, x = 0.52+0.5*0, y = -0.67, hjust = 0, vjust = 0)
full_grid3
full_grid3_rds <- list(rds = full_grid3, width = 20, height = 10)
write_rds(full_grid3, "_fig/002_cohort/002_cohort_umaps_v7.3.rds")
ggsave(filename = "_fig/002_cohort/002_cohort_umaps_v7.3.png", full_grid3, width = 20, height = 10)
ggsave_nodb(filename = "_fig/002_cohort/002_cohort_umaps_v7.3.pdf", full_grid3, width = 20, height = 10)
3 S1
## QC histograms
qc_tbl <- seu_tbl_full %>%
mutate(`log2(#UMIs)` = log2(nCount_RNA),
`log2(#Genes)` = log2(nFeature_RNA),
`log2(#UMIs/#Genes)` = `log2(#UMIs)`-`log2(#Genes)`,
`MT reads [%]` = percent.mt) %>%
select(`log2(#UMIs)`,
`log2(#Genes)`,
# `log2(#UMIs/#Genes)`,
`MT reads [%]`, cell_type) %>%
gather(key, value, -cell_type) %>%
group_by(key) %>%
mutate(median_value = median(value))
qc_tbl <- bind_rows(qc_tbl, mutate(qc_tbl, cell_type = "All cell types")) %>%
mutate(cell_type = ordered(cell_type,
levels = rev(c("All cell types", names(clrs$cell_type)))))
qc_plot <- ggplot(qc_tbl) +
geom_violin(aes(cell_type, value, fill = cell_type),
color = "white", width = 1) +
geom_boxplot(aes(cell_type, value, color = cell_type),
width = 0.15, size = 1, fill = NA, outlier.shape = NA) +
geom_boxplot(aes(cell_type, value), fill = NA,
width = 0.15, color = "white", outlier.shape = NA) +
geom_hline(aes(yintercept = median_value),
data = distinct(qc_tbl, key, median_value)) +
facet_wrap(~key, scales = "free_x", strip.position = "bottom") +
scale_fill_manual(values = c(clrs$cell_type, `All cell types` = "grey10"),
guide = F) +
scale_color_manual(values = c(clrs$cell_type, `All cell types` = "grey10"),
guide = F) +
coord_flip() +
theme(axis.title = element_blank(),
strip.placement = "outside")custom_guide <- guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1, label.position = "right")
mutsig_legend <- cowplot::get_legend(umap_mutsig + theme(legend.title=element_text()) + labs(color = "Mutational\nsignature") + guides(color = custom_guide))
consOV_legend <- cowplot::get_legend(umap_consOV + theme(legend.title=element_text()) + labs(color = "TCGA subtype") + guides(color = custom_guide))
cell_type_legend <- cowplot::get_legend(umap_cell_type + theme(legend.title=element_text(), legend.text = element_text(size = 14, margin = ggplot2::margin(0, 10, 0, 0), color = "black")) + labs(color = "Cell type") + guides(color = custom_guide))
site_legend <- cowplot::get_legend(umap_site + theme(legend.title=element_text()) + labs(color = "Site") + guides(color = custom_guide))
figS1_panel <- ggdraw() +
# draw_plot(add_umap_coord(umap_mutsig + guides(color = F) + labs(title = "Mutational signature")), width = 0.24, height = 0.48, x = 0.01, y = 0.5) +
draw_plot(add_umap_coord(umap_umis), width = 0.24, height = 0.48, x = 0.01, y = 0.5) +
draw_plot(add_umap_coord(umap_genes), width = 0.24, height = 0.48, x = 0.25, y = 0.5) +
draw_plot(add_umap_coord(umap_mitos), width = 0.24, height = 0.48, x = 0.01, y = 0) +
draw_plot(add_umap_coord(umap_phase), width = 0.24, height = 0.48, x = 0.25, y = 0) +
draw_plot(qc_plot, width = 0.5, height = 0.48, x = 0.5, y = 0.5) +
draw_grob(mutsig_legend, x = 0.55, y = 0.48, hjust = 0, vjust = 1) +
draw_grob(consOV_legend, x = 0.55, y = 0.22, hjust = 0, vjust = 1) +
draw_grob(cell_type_legend, x = 0.7, y = 0.48, hjust = 0, vjust = 1) +
draw_grob(site_legend, x = 0.85, y = 0.48, hjust = 0, vjust = 1) +
draw_label("A", x = 0.01, y = 0.98, fontface = "bold", size = 22) +
draw_label("B", x = 0.52, y = 0.98, fontface = "bold", size = 22) +
draw_label("C", x = 0.52, y = 0.48, fontface = "bold", size = 22)
figS1_panel
figS1_panel_rds <- list(rds = figS1_panel, width = 16, height = 9)
ggsave("_fig/002_cohort/002_supplementary_umap_v7.png", figS1_panel, width = 16, height = 9)
ggsave_nodb("_fig/002_cohort/002_supplementary_umap_v7.pdf", figS1_panel, width = 16, height = 9)
write_rds(figS1_panel_rds, "_fig/002_cohort/002_supplementary_umap_v7.rds")